knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

The Package \texttt{ghypernet} is used to run inferential models on multi-edge networks. This vignette guides through the data preparation and model estimation and assessment steps needed to perform network regressions on multi-edge networks. This vignette is split into 3 distinct parts

- Part 1: Data preparation
- Part 2: Running gHypEG regressions
- Part 3: Model assessment, simulations and goodness-of-fit

The vignette builds on a multi-edge network of Swiss members of Parliament. The data set is contained in the package for easy loading. The data set records co-sponsorship activities of 163 members of the Swiss National Council (in German: Natioanlrat). Whenever a member of parliament (MP) drafts a new legislation (or bill), poses a question to the Federal Council (in German: Bundesrat), issues a motion or petition, they are allowed to add co-signatories (or co-sponsors) to the proposal. These co-sponsorship signatures act as a measure of support and signals the relevance of the proposal. As MPs can submit multiple proposals during the course of their service in parliament, each MP can support another MP multiple times, resulting in a multi-edge network of support among MPs.

library(ghypernet) library(texreg, quietly = TRUE) # for regression tables library(ggplot2) # for plotting library(ggraph) #for network plots using ggplot2

After loading the \texttt{ghypernet} package, the included data set on Swiss MPs can be used (already lazy loaded).

data("swissParliament_network", package = "ghypernet")

The data set contains four objects:

**cospons_mat**: contains the adjacency matrix of 163 x 163 MPs. It contains the number of times one MP (rows) supports the submitted proposals of another MP (columns).**dt**: contains different attributes of the 163 MPs, such as their names, their party affiliation (variable:*party*), their parliamentary group affiliation (variable:*parlGroup*), the Canton (or state) they represent (variable:*canton*), their gender (variable:*gender*) and date of birth (variable:*birthdate*).**dtcommittee**: a list of committees each MP was part of during their stay in parliament**onlinesim_mat**: a similarity matrix of how similar two MPs are in their online social media presence (shared supportees).

The above data is coded in an adjacency matrix. However, most often, network data is stored in the more efficient edge list format. Two functions help move from one format to another:

el <- adj2el(cospons_mat, directed = TRUE)

The `adj2el()`

-function transforms an adjacency matrix into an edgelist.
By specifying `directed = FALSE`

, only the top triangle of the adjacency matrix
is stored in the edgelist (making it more efficient to handle,
especially for large networks).

Edgelists also allow you to check basic statics about your network, such as average degree or the degree distribution.

summary(el$edgecount)

The function `el2adj`

transforms an edgelist into an adjacency matrix.

nodes <- colnames(cospons_mat) adj_mat <- el2adj(el, nodes = nodes)

Since edgelists (often) do not store isolate nodes in the network, the function
takes a `nodes`

-attribute. By specifying the nodes attribute, all all nodes
(including isolate nodes) are included in the adjacency matrix.

```
identical(cospons_mat, adj_mat)
```

When preparing nodal attribute data, particular attention has to be given to the
ordering of the two data sets (the adjacancy matrix and the attribute data set).
Testing whether the adjacency matrix and the attribute data are ordered by the
same identifiers (here by the ID codes of the individual MPs, `dt$idMP`

),
attribute-based independent and control variables will correspond with the
dependent variable.

identical(rownames(cospons_mat), dt$idMP)

In case, the above test-code yields `FALSE`

, the attribute data needs to be
ordered.

Let's assume, our attribute data set `dt_unsorted`

is sorted differently:

dt_unsorted <- dt[order(dt$firstName),] identical(rownames(cospons_mat), dt_unsorted$idMP)

The simplest way is to proceed is to create a new data frame with the rownames (or colnames) of the adjacency matrix, then merging the attribute data in.

dtsorted <- data.frame(idMP = rownames(cospons_mat)) dtsorted <- dplyr::left_join(dtsorted, dt_unsorted, by = "idMP") identical(dt$idMP, dtsorted$idMP)

Learn more about data joins here.

To estimate effects of endogenous and exogenous factors (i.e., independent and control variables) on the multi-edge network, covariates have to be fed into the gHypEG regression as matrices with the same dimensions as the multi-edge network (i.e., the dependent variable).

dim(cospons_mat) == dim(onlinesim_mat)

Additionally, it is prudent to make sure that all covariates have the same row- and column-names:

table(rownames(cospons_mat) == rownames(onlinesim_mat))

The gHypEG regression is zero-sensitive. Zero value entries in covariates signify structural zeros and are not considered in the estimation process. Therefore, all zero-values that do not signify structural zeros need to be recoded. To simplify interpretation of coefficient sizes in the regression, a minimum value divided by a factor of 10 is the simplest substitution.

For instance, if a dummy variable takes 0, 1 values in a matrix, all 0 values should be recoded to 0.1. If endogenous network statics are calculated, these zero-values are automatically generated (see below).

Change statistics (or change scores) can be used to model endogenous network properties in inferential network models [@snijders2006new, @hunter2008goodness, @krivitsky2011adjusting]. For each dyad in the multi-edge network, the change statistic caputres the (un-)weighted values of additional edges involved in the interested network pattern. See Brandenberger et al. [-@brandenberger2019quantifying] for additional information on change statistics for multi-edge networks.

The `reciprocity_stat()`

-function can be used to calculate weighted
reciprocity change statistic. Since it's dyad-independent, it can be used as
a predictor in the gHypEG regression.

The function takes either a matrix or an edgelist. If an edgelist is provided,
the `nodes`

-object can be specified again to ensure that isolates are included
as well.

recip_cospons <- reciprocity_stat(cospons_mat) recip_cospons[1:5, 1:3]

The resultant matrix measures reciprocity by checking for each dyad $(i, j)$, how many edges were drawn from $(j, i)$. If reciprocity is a driving force in the network, taking the transpose of the matrix should correlate strongly with the co-sponsorship matrix.

The `zero_values`

-argument allows for the specification of minimum values. By
default, the minimum edgecount in the network divided by a factor of 10 is
used (here 1/10 = .1). This simplifyies interpretation of coefficient sizes.

The `sharedPartner_stat()`

-function provides change statistics to check your
multi-edge network for meaningful triadic closure effects.
Triadic closure refers to the important tendency observed in social networks
to form triangles, or triads, between three nodes $i$, $j$ and $i$.
If dyad $(i, k)$ are connected, and dyad $(j, k)$ are connected, there is a
strong tendency in some social networks that dyad $(i, j)$ also shares an edge
(see Figure 1a and 1b).

The `sharedPartner_stat()`

-function uses the concept of shared partner statistics
to calculate the tendencies of nodes in multi-edge networks to re-inforce
triangular structures (see Figure 1c).

knitr::include_graphics('images/tikz_nweffects.pdf')

For undirected multi-edge networks, the statistic measures for each dyad $(i, j)$---regardless of whether or not $(i, j)$ share edges or not---how many shared partners $k$ both nodes $i$ and $j$ have in common.

If the option `weighted = FALSE`

is specified, the raw number of shared partners $k$
is reported in the shared partner matrix. For dense multi-edge networks, this
statistic is not meaningful enough (since all dyads share at least one
edge in a complete graph) to examine meaningful triadic closure. The option
`weighted = TRUE`

therefore calculates a weighted shared partner statistic, where
edge counts are taken into consideration as well (min(edgecount(i, k), edgecount(j, k))) [see @brandenberger2019quantifying].

shp_cospons_unweighted <- sharedPartner_stat(cospons_mat, directed = TRUE, weighted = FALSE) shp_cospons_unweighted[1:5, 1:3] shp_cospons_weighted <- sharedPartner_stat(cospons_mat, directed = TRUE) shp_cospons_weighted[1:5, 1:3]

For directed multi-edge networks, the option `triad.type`

allows for two more
specialized shared partner statistics: incoming and outgoing shared partners.
Assume dyad $(i, j)$ have shared partner $k$ in common. For `triad.type = "incoming"`

,
it is assumed that $k$ ties to $i$ and $j$ (= edges $(k, i)$ and $(k, j)$ are present).
In the co-sponsorship example, this measures whether nodes $i$ and $j$ are likely
to support each other, if they both are supported by the same other node/s $k$.
For `triad.type = "outgoing"`

, it is assumed that $i$ and $j$ both tie to $k$
(regardless of whether $k$ also ties to $i$ or $j$). In other words, for outgoing-
shared partners, for dyad $(i, j)$, we check whether edges $(i, k)$ and $(j, k)$
are present.

shp_cospons_incoming <- sharedPartner_stat(cospons_mat, directed = TRUE, triad.type = 'directed.incoming') shp_cospons_incoming[1:5, 1:3] shp_cospons_outgoing <- sharedPartner_stat(cospons_mat, directed = TRUE, triad.type = 'directed.outgoing') shp_cospons_outgoing[1:5, 1:3]

The `homophily_stat()`

-function can be used to calculate homophily tendencies in
the multi-edge network. Homophily represents the tendency of nodes with similar
attributes to cluster together (i.e., nodes interact more with similar other nodes
than dissimilar ones) [see @mcpherson2001birds].
The function can be used for categorical and continuous
attributes.

If a categorical attribute is provided (in the form of a `character`

or `factor`

variable), `homophily_stat()`

creates a homophily matrix, where nodes of the same
attribute are set to 10 and dyads with nodes of dissimilar attributes are set 1.

canton_homophilymat <- homophily_stat(dt$canton, type = 'categorical', nodes = dt$idMP) canton_homophilymat[1:5, 1:3]

The option `these.categories.only`

can be used to specify which categories in the
attribute variable should lead to a match. For instance, if you'd only like to test
whether parliamentary members from the canton Bern exhibit homophily tendencies,
you can specify:

canton_BE_homophilymat <- homophily_stat(dt$canton, type = 'categorical', nodes = dt$idMP, these.categories.only = 'Bern')

You can also specify multiple matches, e.g.:

canton_BEZH_homophilymat <- homophily_stat(dt$canton, type = 'categorical', nodes = dt$idMP, these.categories.only = c('Bern', 'Zuerich'))

The matrix `canton_BEZH_homophilymat`

now reports homophily values of 10 for dyads of
MPs who are both from Bern or both from Zurich, compared to all other dyads (set to 1).

Apart from cantonal homophily, party, parliamentary groups, gender and age homophily may play a role in co-sponsorship interactions.

party_homophilymat <- homophily_stat(dt$party, type = 'categorical', nodes = dt$idMP) parlgroup_homophilymat <- homophily_stat(dt$parlGroup, type = 'categorical', nodes = dt$idMP) gender_homophilymat <- homophily_stat(dt$gender, type = 'categorical', nodes = dt$idMP)

If a numeric variable is provided, the `homophily_stat()`

-function calculates
absolute differences for each dyad in the network.

dt$age <- 2019 - as.numeric(format(as.Date(dt$birthdate, format = '%d.%m.%Y'), "%Y")) age_absdiffmat <- homophily_stat(dt$age, type = 'absdiff', nodes = dt$idMP) age_absdiffmat[1:5, 1:3]

For each dyad $(i, j)$, the age of $i$ and $j$ are subtracted and the absolute value is used in the resultant homophily matrix. It is important to note that the absolute difference statistic is slightly counter-intuitive, since small differences indicate stronger homophily. In the gHypEG regression, this presents as a negative coefficient for strong homophily tendencies.

The `zero_values`

-option can again be used to specify your own zero-values replacements.

Generally, any meaningful matrix with the same dimension as the dependent variable (i.e., here the co-sponsorship matrix) can be used as a covariate in the gHypEG regression [see @casiraghi2017multiplex].

An example: the data frame `dtcommittee`

contains information on which
committees each MP served on during their time in office.

```
head(dtcommittee)
```

One potential predictor for co-sponsorship support may be if two MPs shared the same committee seat. When preparing your own matrices, make sure the row- and columnnames match the depdenent variable (here the co-sponsorship matrix).

## This is just one potential way of accomplishing this! identical(as.character(dtcommittee$idMP), rownames(cospons_mat)) shared_committee <- matrix(0, nrow = nrow(cospons_mat), ncol = ncol(cospons_mat)) rownames(shared_committee) <- rownames(cospons_mat) colnames(shared_committee) <- colnames(cospons_mat) for(i in 1:nrow(shared_committee)){ for(j in 1:ncol(shared_committee)){ committees_i <- unlist(strsplit(as.character(dtcommittee$committee_names[i]), ";")) committees_j <- unlist(strsplit(as.character(dtcommittee$committee_names[j]), ";")) shared_committee[i, j] <- length(intersect(committees_i, committees_j)) } } shared_committee[shared_committee == 0] <- 0.1 # replace zero-values shared_committee[1:5, 1:3]

The gHypEG regression accounts for combinatorial effects, i.e., degree distributions. Compared to other inferential network models, it is therefore not neccessary to specify (out/in-)degree variables. The model can be estimated using average expected degrees. In this case it is wise to specify a degree control matrix:

dt$degree <- rowSums(cospons_mat) + colSums(cospons_mat) degreemat <- cospons_mat for(i in 1:nrow(cospons_mat)){ for(j in 1:ncol(cospons_mat)){ degreemat[i, j] <- sum(dt$degree[i], dt$degree[j]) } } degreemat[degreemat == 0] <- .1

It is also not neccessary to control for activity (outdegree) and popularity (indegree) of different node groups in the standard gHypEG regression. However, if you'd like to test for these effects (because they are part of your hypothesis), the gHypEG regression can be estimated with average expected degrees (i.e., without the degree correction).

For attribute-based outdegree measures, create custom matrices:

age_activity_mat <- matrix(rep(dt$age, ncol(cospons_mat)), nrow = nrow(cospons_mat), byrow = FALSE) svp_activity_mat <- matrix(rep(dt$party, ncol(cospons_mat)), nrow = nrow(cospons_mat), byrow = FALSE) svp_activity_mat <- ifelse(svp_activity_mat == 'SVP', 10, 1)

For attribute-based indegree measures, create custom matrices:

age_popularity_mat <- matrix(rep(dt$age, ncol(cospons_mat)), nrow = nrow(cospons_mat), byrow = TRUE) svp_popularity_mat <- matrix(rep(dt$party, ncol(cospons_mat)), nrow = nrow(cospons_mat), byrow = TRUE) svp_popularity_mat <- ifelse(svp_popularity_mat == 'SVP', 10, 1)

The gHypEG regression can be estimated using the `nrm()`

-function.
The function takes

fit <- nrm(adj = cospons_mat, w = list(reciprocity = recip_cospons), directed = TRUE, selfloops = FALSE, regular = FALSE)

The `adj`

-object takes the adjacency matrix of the multi-edge network (i.e.,
the dependent variable).
The `w`

-object (stands for weights) takes the list of covariates. All
covariates can be combined into one list. The list can be named for a better
overview in the regression output.
The `directed`

-argument can either be `TRUE`

or `FALSE`

. If set to `TRUE`

, the
multi-edge network under consideration is directed in nature.
The `selfloops`

-argument can either be `TRUE`

or `FALSE`

. If set to `TRUE`

,
self-loops are considered possible in the network. In the case of co-sponsorship
support signatures, self-loops are not possible by definition and should therefore
be excluded from the analysis.
In the case of a citation network, however, self-loops are possible and meaningful and
should be included from the analysis.
The `regular`

-argument can either be `TRUE`

or `FALSE`

. If set to `TRUE`

, the
gHypEG regression is estimated with estimated average degrees (specified with the
`xi`

-matrix) instead of with the automatic control for combinatorial effects.

Initial values for the weights can be specified in the gHypEG regression.
These initial values help the estimation process to speed up the estimation
process even more.
Alternatiely, these initial values can be calculated endogenously. The
`XXTOOD`

-function can be used for this purpose. It automatically sets
the appropriate initial values by going through a stepwise process first.

The `texreg`

-package can be used to export regression tables.

Co-sponsorship networks have been shown to be structured by reciprocity [@cranmer2011inferential]. Several empirical studies have shown that co-sponsorship networks also exhibit tendencies towards triadic closure [@tam2010legislative]. However, Brandenberger [-@brandenberger2018trading] shows that when estimating co-sponsorship networks as bipartite graphs, the triadic closure effect is non-existant. @craig2015role show that homophily also plays an important role in co-sponsorship networks. We therefore use these predictors to estimate the effect of MPs supporting each other's bills in parliament.

nfit1 <- nrm(adj = cospons_mat, w = list(same_canton = canton_homophilymat), directed = TRUE) summary(nfit1)

To speed things up, the `init`

-argument can be specified:

nfit1 <- nrm(adj = cospons_mat, w = list(same_canton = canton_homophilymat), directed = TRUE, init = c(0.09)) summary(nfit1)

texreg::screenreg(nfit1)

The variable `same_canton`

shows a positive coefficient and is significant.
The coefficient of $0.09$ can be interpreted as follows: The odds of MP $i$
co-sponsoring the bill of MP $j$ increase by a factor of $1.23$
($(10^{0.09})/(1^{0.09}) = 1.23$) if $i$ and $j$ are representatives from the
same canton. Since the baseline of the dummy covariate `same_canton`

is 1, the
odds can be calculated by exponating the coefficient over the treatment value (here 10).

nfit2 <- nrm(adj = cospons_mat, w = list(reciprocity = recip_cospons, #sharedpartner_in = shp_cospons_incoming, #sharedpartner_out = shp_cospons_outgoing, party = party_homophilymat, canton = canton_homophilymat, gender = gender_homophilymat, age = age_absdiffmat, committee = shared_committee, online_similarity = onlinesim_mat ), directed = TRUE, init = c(.1, .5, .1, 0, 0, 0, 0))

screenreg(nfit2, groups = list('Endogenous' = 1, 'Homophily' = c(2:5), 'Exogenous' = c(6:7)))

XXTODO: add the find-init version here. Add the triad-function. Then interpret some coefficients.

nfit3 <- nrm(adj = cospons_mat, w = list(degree = degreemat, reciprocity = recip_cospons, #sharedpartner_in = shp_cospons_incoming, #sharedpartner_out = shp_cospons_outgoing, party = party_homophilymat, svp_in = svp_popularity_mat, svp_out = svp_activity_mat, canton = canton_homophilymat, gender = gender_homophilymat, age = age_absdiffmat, agein = age_popularity_mat, ageout = age_activity_mat, committee = shared_committee, online_similarity = onlinesim_mat), directed = TRUE, regular = TRUE, init = c(0, 0.1, 0.5, 0, 0, .1, 0, 0, 0, 0, .1, .01)) summary(nfit3)

Comparing the two models:

screenreg(list(nfit2, nfit3), custom.model.names = c('with degree correction', 'without deg. cor.'))

Model comparisons can be done using `AIC`

-scores, LR-tests or the R-squared
measures.
AIC-scores are the best indicators of model fit. The gHypEG model can also be
fit maximally to the data. This perfectly fit model cannot be interpreted (since
step by step, additional predictive layers are added and these laysers capture
deviances but would need to be interpreted individually), but the AIC scores
can be used to check how far away your models are from it.

fullfit <- ghype(graph = cospons_mat, directed = TRUE, selfloops = FALSE)

The omega matrix stored in the `nrm`

-object holds XXTODO.
It can be used to calculate marginal effects.

nfit2omega <- data.frame(omega = nfit2$omega, cosponsfull = c(cospons_mat), age_absdiff = c(age_absdiffmat), sameparty = c(party_homophilymat)) nfit2omega[nfit2omega == 0] <- NA nfit2omega <- na.omit(nfit2omega)

ggplot(nfit2omega, aes(x = age_absdiff, y = omega, color = factor(sameparty)))+ geom_point(alpha = .1) + geom_smooth() + theme(legend.position = 'bottom') + scale_color_manual("", values = c('#E41A1C', '#377EB8'), labels = c('Between parties', 'Within party'))+ xlab("Age difference") + ylab("Tie propensities")+ ggtitle('Model (2): Marginal effects of age difference')

The `rghype()`

-function simulates networks form `nrm`

-models.
The number of simulations can be specified with the `nsamples`

argument.

simnw <- rghype(nsamples = 1, model = nfit2, seed = 1253)

ggraph(graph = simnw, layout = 'stress') + geom_edge_link(aes(filter = weight>5, alpha=weight)) + geom_node_point(aes(colour = dt$parlGroup), size=10*apply(simnw,1,sum)/max(apply(simnw,1,sum))) + scale_colour_manual("", values = c('orange', 'yellow', 'blue', 'green', 'grey', 'darkblue', 'red', 'darkgreen', 'purple')) + theme(legend.position = 'bottom') + coord_fixed() + theme_graph()

One potential way of performing goodness of fit measures for inferential network models is to simulate new networks based on the regression model and compare structural properties of the simulated networks to the original network [@hunter2008goodness]. We are currently working on finding appropriate multi-edge network statistics to assess model fit for gHypEGs. We are also looking into alternative ways of assessing network model fit. Stay tuned!

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